A team of German researchers has identified 1H-nuclear magnetic resonance (NMR)-based urinary metabolic profiling as a potential diagnostic and prognostic biomarker in spinal muscular atrophy (SMA), according to a study published in the Orphanet Journal of Rare Diseases.

One of the major problems hampering the treatment of SMA is that physicians cannot reliably predict disease severity. “Due to the absence of predictive biomarkers, treatment decisions are currently based on quantification of SMN2 copy numbers, with moderate genotype-phenotype correlation,” the research team wrote.

The German researchers recognized that a way to remedy this problem is to identify reliable biomarkers that can predict disease severity. Urinary metabolic profiling has already been used to identify reliable biomarkers in a variety of central nervous system diseases.


Continue Reading

“The aim of this proof-of-concept study was to use a highly standardized and strictly quality controlled NMR-based metabolomics platform to further explore whether urinary metabolic signatures can serve as additional biomarkers for diagnosis and disease prediction in SMA,” they wrote. 

Read more about SMA etiology

The researchers collected urine samples from 29 treatment-naïve SMA patients, 18 patients with Duchenne muscular dystrophy (DMD), and 444 healthy individuals for the control group. They then identified noninvasive diagnostic fingerprints using a 1H-NMR-based metabolomics approach to analyze urine samples of the 24 SMA patients compared to an age-matched control group.

“Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity,” the authors said.

The findings of this study have the potential to be used in a wide variety of circumstances. “Our results demonstrate that 1H-NMR-based metabolic profiling of urinary samples is feasible and might hold the potential to complement current treatment algorithms in SMA, providing an additional tool for diagnosis, disease prediction, therapeutic decision-making, and follow-up under treatment,” the researchers concluded.

Reference

Saffari A, Cannet C, Blaschek A, et al. 1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophyOrphanet J Rare Dis. Published online October 20, 2021. doi:10.1186/s13023-021-02075-x